Exploring regularities in software with statistical models and their applications
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The Department of Electrical and Computer Engineering (ECpE) contains two focuses. The focus on Electrical Engineering teaches students in the fields of control systems, electromagnetics and non-destructive evaluation, microelectronics, electric power & energy systems, and the like. The Computer Engineering focus teaches in the fields of software systems, embedded systems, networking, information security, computer architecture, etc.
History
The Department of Electrical Engineering was formed in 1909 from the division of the Department of Physics and Electrical Engineering. In 1985 its name changed to Department of Electrical Engineering and Computer Engineering. In 1995 it became the Department of Electrical and Computer Engineering.
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1909-present
Historical Names
- Department of Electrical Engineering (1909-1985)
- Department of Electrical Engineering and Computer Engineering (1985-1995)
Related Units
- College of Engineering (parent college)
- Department of Physics and Electrical Engineering (predecessor)
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Abstract
Software systems are becoming popular. They are used with different platforms for different applications. Software systems are developed with support from programming languages, which help developers work conveniently. Programming languages can have different paradigms with different form, syntactic structures, keywords, and representation ways. In many cases, however, programming languages are similar in different important aspects: 1. They are used to support description of specific tasks, 2. Source codes are written in languages and includes a limit set of distinctive tokens, many tokens are repeated like keywords, function calls, and 3. They follow specific syntactic rules to make machine understanding. Those points also respect the similarity between programming language and natural language.
Due to its critical role in many applications, natural language processing (NLP) has been studied much and given many promising results like automatic cross-language translation, speech-to-text, information searching, etc. It is interesting to observe if there are similar characteristics between natural language and programming language and whether techniques in NLP can be reused for programming language processing? Recent works in software engineering (SE) shows that their similarities between NLP and programming language processing and techniques in NLP can be reused for PLP.
This dissertation introduces my works with contributions in study of characteristics of programming languages, the models which employed them and the main applications that show the usefulness of the proposed models. Study in both three aspects has drawn interests from software engineering community and received awards due to their innovation and applicability. '
I hope that this dissertation will bring a systematic view of how advantage techniques in natural language processing and machine learning can be re-used and give huge benefit for programming language processing, and how those techniques are adapted with characteristics of programming language and software systems.